Presently eye tracking has been utilized in neuropsychological testing to measure cognitive performance when a test taker uses his eyes to track a moving icon or dot which moves along a prescribed smooth pursuit path as described in U.S. patent application Ser. Nos. 13/694,873; 13/694,462; 13/694,461; 13/507,991 and 13/506,840 all incorporated herein by reference. The state of the art in cognitive performance testing has provided clinicians and researchers with a more stable and accurate assessment of cognitive ability than heretofore possible, utilizing either the headset mounted apparatus of U.S. patent application Ser. No. 13/506,840 or the desktop apparatus described in U.S. patent application Ser. No. 13/507,991. It is now possible to monitor cognitive performance through the use of a number of measuring metrics, the most successful of which has been measuring the lead time or lag time of the eye as the eye attempts to track the smooth pursuit moving object. The lag time or lead time measurements while measuring cognitive performance may be improved upon by utilizing other ways of analyzing the data to pinpoint not only cognitive performance but also regions of the brain responsible for the cognitive performance results.
According to U.S. Pat. No. 7,819,818 by Jamshid Ghajar, one can use baselining to detect cognitive performance of a patient tracking a smooth pursuit object, usually utilizing standard deviation techniques. However, baselining as a metric, while useful, is not the only metric that is useful in measuring cognitive performance. As will be discussed, other metrics may be employed to pinpoint regions of the brain responsible for various cognitive impairment or abilities. It is only because of the use of the devices described in U.S. patent application Ser. Nos. 13/506,840 and 13/507,991 that one has the ability to robustly measure cognitive performance by any metric.
While the above describes current smooth pursuit eye tracking techniques, given the sensitivity and reliability of the newer neuropsychological testing techniques the following discussion details the state of the art in a generic manner so as to be able to describe the new measuring metrics of the subject invention.
Current state of the art and the analysis of cognitive variability, whether it's eye tracking or mechanical motion, is to use a data set consisting the x, y and t locations of the user's or the patient's eyes, finger or other body part that is being used to test cognition. This data is collected by either a mechanical system, or an eye tracker for instance looking at the eye, or by some analysis of sensors on the body as the body is moving in response to a test. The underlying premise of the analysis that is currently done is that the user is attempting to replicate with his or her own body, the motion or movement of a moving object that is moving in a way that is known as smooth pursuit movement. The resulting data file captures how well that patient is following or tracking the smooth pursuit-moving object.
There are a number of different paradigms for capturing the smooth pursuit eye movement: eye tracking, mechanical movement and a hybrid movement approach that is a combination of eye tracking and mechanical movement.
Eye Tracking
The eye tracking smooth pursuit eye movement analysis paradigm suggest that the test taker should follow a smoothly moving dot as it tracks across a display or screen with their eyes while cameras measure how well the eyes are moving in response to tracking the dot. It is presumed that this function requires higher order cognitive function in multiple parts of the brain from the eye, the optical cortex, the prefrontal lobe and the portion of the brain responsible for moving eye muscles. As the eye moves, the eye tracker trained on the eyes of the test taker generates the x, y location of the dark pupil, or light pupil, as well as the x and y location of the corneal reflection. This process of calculating the gaze of the test taker, called gaze transformation, tracks where the test taker's eyes was actually looking that can be used to analyze how far off the test taker was from the target he or she was supposed to be looking at.
Mechanical Movement
The hybrid movement is a hybrid approach of eye tracking and mechanical movement. The hybrid movement is the process by which a test is administered on a screen where the test taker is looking at the test on the screen to smoothly track the moving target on the screen with his or her eyes while simultaneously moving their hand, or other body part, to mechanically track the target. An input source is used to capture the movement of at least one or more points on the body for instance via an input source, such as a mouse, tablet, stylus, keyboard or turn wheel, as one takes the test.
This method of generating data files also produces a number of different opportunities for data analysis. The most specific data analysis is the comparison of the location of the primary source of mechanical input and the location of the target projected on the screen, which assesses the difference between where the target was and where the user was. This method in the hybrid sense can also be coupled with an eye tracker to generate a secondary set of data, where the test taker was actually looking during the test. This secondary set of data may even be two sets of data when individual data is taken per eye, one for the left eye and another for the right eye.
Independent of how smooth pursuit eye movement data was captured, the resulting data file typically contains x, y locations and time stamps of the test target as well as that of the test-taker. Currently the smooth pursuit eye movement data is analyzed for their continuity of motion, how well the test-taker follows the predicted path of the target controlled by the test administrator or test designer. The target path is of a smooth linear motion, which is sometimes curved and often times a motion that has a fixed speed over time.
Today the dominant method of analyzing the opto-cognitive test data files with respect to assessing cognitive performance or cognitive impairment is a metric known as smooth pursuit variability analysis. Variability analysis is the process of taking the differences in x, y, and radial distances between the target location and the patient's attempt to replicate that target location, which is measured by the tangents of the point distances between the target and the patient's attempt to replicate, then applying the standard deviation function that assesses what the degree of variability, or variance squared, are across the distances over the duration of the test. Thus, the degree of variability assesses whether the movements are at a predictable interval ahead or behind the target, or the movements are moving at variable distances sometimes behind and sometimes ahead of the target. A high degree of variability is reflected in a high standard deviation score.
This variability analysis calculation however is not a trivial one. Often times the input data source has noise or errors in the data that complicate the analysis. For example, for eye tracking data sets, the users blinking or saccading as covered in the prior art can introduce sources of noise, which must be detected and filtered out. For the mechanical movement and hybrid movement data sets, motion or mechanical sources of noises like spasms also need to be taken into consideration. Mechanical sources of noise may derive from various physical variables of the test-taker such as the test-taker's metabolic rate and the state of the muscle that would impact the smoothness of the data. Mechanical sources of noise too must be detected and filtered out in the data file. Once the data file has been filtered, the data must be smoothed in some way before it is ready for analysis. Smoothing the data in other words is to find and concatenate long runs of continuous data that are uninterrupted into a single sequence of data that can then be used for the purposes of analysis.
However, there is a problem with variability analysis. In variability calculation, the variability is compressed by the use of standard deviation formulas, which brings the test file down to a single or couple of numbers to characterize the test performance of the patient. Despite the objective of the application of standard deviation in the variability analysis of capturing the degree to which the patient is matching the location of the target in the smooth pursuit test, standard deviation unfortunately does not perform as desired, especially in cases where the patient is not doing well. This is because the standard deviation is a convex function. This means that the standard deviation function does not have an upper bound limit. Thus, the standard deviation function does not have an upper bound for how badly the patient performed on the test, but if the patient performs the test perfectly, the standard deviation will reflect a score of zero or an extremely low value. As a result, for every test and every patient, a baseline must be taken and the machine must be calibrated with respect to the frame rate per second, the time lag in between consecutive dots, and the magnitude of the difference in the dot in terms of pixels or degrees in order to first filter out those instances or at least to calibrate those instances where the standard deviation might be a large score. This is done to establish a relative upper bound of the standard deviation unique to the device and the patient. In fact, this creates a secondary metric, for instance a one to ten linear scale that can translate a standard deviation raw output score into a limited linearized scoring system based on the patient's performance.
Other simpler metrics other than variability analysis, or standard deviation, are sometimes employed in the analysis of these data files. For instance, mean, median, mode and other statistical measures are used to determine outliers usually assess the quality of the captured data file, run length sequence, and consecutive run length sequence. These measurements are generally used to assess the performance quality of the test-taker, quality of the data file, and the performance of the test to capture the patient's attempt to replicate the test without calculating cognitive performance. Such measurements have been described in the prior art.